Optimal Placement and Sizing of Electric Vehicle Charging Infrastructure in a Grid-Tied DC Microgrid Using Modified TLBO Method

Abstract

In this work, a DC microgrid consists of a solar photovoltaic, wind power system and fuel cells as sources interlinked with the utility grid. The appropriate sizing and positioning of electric vehicle charging stations (EVCSs) and renewable energy sources (RESs) are concurrently determined to curtail the negative impact of their placement on the distribution network’s operational parameters. The charging station location problem is presented in a multi-objective context comprising voltage stability, reliability, the power loss (VRP) index and cost as objective functions. RES and EVCS location and capacity are chosen as the objective variables. The objective functions are tested on modified IEEE 33 and 123-bus radial distribution systems. The minimum value of cost obtained is USD 2.0250 × 106 for the proposed case. The minimum value of the VRP index is obtained by innovative scheme 6, i.e., 9.6985 and 17.34 on 33-bus and 123-bus test systems, respectively. The EVCSs on medium- and large-scale networks are optimally placed at bus numbers 2, 19, 20; 16, 43, and 107. There is a substantial rise in the voltage profile and a decline in the VRP index with RESs’ optimal placement at bus numbers 2, 18, 30; 60, 72, and 102. The location and size of an EVCS and RESs are optimized by the modified teaching-learning-based optimization (TLBO) technique, and the results show the effectiveness of RESs in reducing the VRP index using the proposed algorithm.

A hybrid optimization approach for strategically placing electric vehicle charging stations in a radial distribution IEEE-33 bus system

“…A strategic plan for the placement of electric vehicle charging stations that supports the growing adoption of EVs while maintaining the reliability and efficiency of the power distribution system is likely to be proposed using this approach, which likely takes into account a number of factors including electrical load, network stability, and accessibility. Furthermore, the goal function is compared to other methods in the literature [31,38,39,43,44,45,46,47,48]. This shows that the hybrid optimizer presented in this research shows more better results than the other state of the art techniques.…”

Modelling and Allocation of Hydrogen-Fuel-Cell-Based Distributed Generation to Mitigate Electric Vehicle Charging Station Impact and Reliability Analysis on Electrical Distribution Systems

“…The article presents a valuable contribution to the ideal appointment and sizing of EVCSs in a grid-tied DC microgrid. The research findings offer insights into the efficient integration of EVs and RES, contributing to the development of sustainable and resilient microgrids [21]. The results of the study demonstrate the efficiency of the planned optimization approach in achieving the appropriate allotment of EVCS and DG units in an RDS.…”

Quality of Service and Associated Communication Infrastructure for Electric Vehicles

“…Intelligent charging technology can be employed to control the charging profiles of electric vehicles, achieving a lower operational cost or peak-to-average power consumption ratio for the charging stations. In [59,80], the author proposes an optimization framework to reduce the operating costs for charging stations by achieving a certain level of QoS for the electric vehicles, thereby addressing the sizing issue. The author in [81] developed a divided rectangle method for better vehicle fuel efficiency and drivability and the optimization algorithm solved a multi-input, multi-output problem using a state-space model.…”